AYESHA IRFAN

Academic

Career

Verbatim text

Linkedin Profile
AYESHA IRFAN
a.irfan.24818@khi.iba.edu.pk
321/227-9249/0321-2451975
BSAF
CAREER OBJECTIVE
Looking forward to a role that will be give me a chance to showcase and enhance my skills and expertise in Accounting and Finance.
EDUCATION
DEGREE/PROGRAM
YEAR OF GRADUATION
INSTITUTION
CGPA/PERCENTAGE
City
BSAF
June 2025
Institute of Business Administration, Karachi
3.76
A-Level
2021
The City School, PAF Chapter
A*, 2As, B
Karachi
o-level
2019
Other, Karachi
5As, 2Bs, C
MAJORS
Accounting & Finance
INTERNSHIP/WORK EXPERIENCE(S)
DESIGNATION
NAME OF THE COMPANY
TENURE/DURATION
IBA AMAN Center For Entrepreneurial Development
27 May 2022 - 10 Aug 2024
Spearheaded global promotion efforts, establishing contact with embassies, and consulates in 100+ countries. Developed and managed
robust database encompassing countries, universities, and societies optimizing outreach capacities.
Agora
15 Feb 2024 - 15 Aug 2022
Wrote an in-depth analysis of Business Studies O-level Paper 1 exam questions of all variants from 2011 to 2015 to aid prospective
students in exam preperation.
SKILLS AND COMPETENCIES
INTEREST AND HOBBIES
TERM PROJECT(S)/RESEARCH WORK(S)
Lucky Core Industries | Financial Model: Forecasting five year’s sales, finance cost, and depreciation based on historical data through
regression, and trend analysis.
ICAP | Organizational Behavior Project: Analyzed the challenge of low employee retention rate at ICAP, with 4 members, through both
primary and secondary research. Designed and implemented an employee survey to gather feedback on their perspective and
preferences. Developed data-driven recommendations to address the issue.
Olper’s Full Cream Milk | Marketing Project: Developed an extensive report with 3 other members on the Olpers’ market position,
customer base, and successful marketing campaign. Conducted both primary and secondary research, including an interview with the
brand manager as the key component of the primary research.
Spaceship Titanic | Kaggle Competition: Employed random forest and gradient booster algorithm to predict the target variable with an
accuracy of 80.57%.

AI enrichment